We adopt the TCN module in Conv-TasNet[1]} with STFT as the encoder, iSTFT as the decoder, and use 4 repeated stacks each having 6 1-D convolutional blocks in the masking network. STFTs are computed with a window size of 32 ms and a hop size of 8 ms. The effective receptive field of the model is approximately 4 s. For computational efficiency, only 8 of the channels (4 from each array) were used to train the MCSDN.
